XRT: Programming-Language Independent MapReduce on Shared-Memory Systems

Erik G. Selin, H. Viktor
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Abstract

Increasing processor core-counts have created an opportunity for efficient parallel processing of large datasets on shared-memory systems. When compared to clusters of networked commodity hardware, shared-memory systems have the potential to provide better per-core performance, a more straightforward development environment and reduced operational overhead. This paper presents XRT, a high-performance and programming-language independent MapReduce runtime for shared-memory systems. XRT is built to be simple to use, pedantic about resource usage and capable of utilizing disk-based data structures for processing datasets too large to fit in memory. To our knowledge, XRT is the first MapReduce runtime explicitly designed for programming-language independent MapReduce. Moreover, XRT is the first MapReduce runtime for shared-memory systems taking advantage of disk-based data structures for processing datasets which cannot fit in memory. Benchmarks of three common data processing problems demonstrate the disk-based capabilities as well as the excellent speedup profile of XRT as system core-counts increase.
XRT:独立于编程语言的共享内存系统MapReduce
不断增加的处理器核数为在共享内存系统上高效并行处理大型数据集创造了机会。与联网的商用硬件集群相比,共享内存系统有可能提供更好的每核性能、更直接的开发环境和更少的操作开销。本文介绍了XRT,一个用于共享内存系统的高性能且独立于编程语言的MapReduce运行时。XRT被构建为易于使用,对资源使用情况比较严谨,并且能够利用基于磁盘的数据结构来处理大到无法装入内存的数据集。据我们所知,XRT是第一个明确为独立于编程语言的MapReduce设计的MapReduce运行时。此外,XRT是第一个用于共享内存系统的MapReduce运行时,它利用基于磁盘的数据结构来处理内存无法容纳的数据集。三个常见数据处理问题的基准测试表明,随着系统核心数的增加,XRT具有基于磁盘的能力和出色的加速性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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